• Task-based learning.
• Quick start + References.
• Plan: Skim through some examples and work closely on two of them.

• Exploratory analysis

  • Get a sense of what data looks like
  • Help you choose (parametric) models
  • Help you engineer features

• Confirmatory analysis

  • Model fit evaluation
  • Residual analysis

Data: Copenhagen Reinsurance 2167 fire losses records from 1980 to 1990.

library(MASS)
model <- fitdistr(danish, 'lognormal')

Data: Death numbers, ~100 features, a few K datapoints.

lognormal_model <- fitdistr(danish, 'lognormal')
gamma_model <- fitdistr(danish, 'gamma')
print(c(lognormal_model$loglik, gamma_model$loglik))

## [1] -4433.891 -5243.027

Hard to understand what they actually mean!

plot(x,y, type = 'l')
lines(x, fitted(lm_model), col = 'red', lwd = 2)

res <- y - fitted(lm_model)
plot(x, res, type = 'l', ylim = c(-0.4, 0.4))

Fairly clear we have missed something.

?COMMAND_NAME, e.g.

?max

install.packages("PACKAGE_NAME"), e.g.

install.packages("d3heatmap")

"2013 - current - future" regime:

• magrittr, tidyr, dplyr

\(\\\)

"2016 - future" regime :

• broom, purrr

"Data sets come in many formats…but R prefers just one." - Garrett Grolemund

(See other slides.)

Reference: data wrangling with R https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/

• Task 1. What do my data look like? The 'tabplot' package.
• Task 2. Are my predictor variables correlated? The 'corrplot' package.
• Task 3. I need professional graphics for publishing purposes. The 'ggplot2' package.

library(ggplot2)
data(diamonds)

library(tabplot)
tableplot(diamonds)

load('input_output//cleaned_data', verbose = T)

library(corrplot)
correlation_matrix <- cor(data1[,10:30]) 
corrplot(correlation_matrix, order = "hclust", addrect = 8)

## Loading objects:
##   data1

• Professional plots are not quick plots.

• Always have your cheatsheets right next to you.

  • https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
  • https://flowingdata.com/2015/03/17/r-cheat-sheet-for-graphical-parameters/
• Leverage what you have

• Do not over-customise your graphics

References:

• http://theinformationcapital.com/coder-designer/
• http://spatial.ly/2014/11/r-visualisations-design/

We are going to create a plot similar to this:

Specialized packages:

• d3heatmaps, leaflet (for maps), DT (for tables)
• They do what they say they do. Nothing more, nothing less.

General packages:

• plotly, ggvis, rCharts, googleVis
• See review @ http://www.r-bloggers.com/interactive-visualizations-with-r-a-minireview/

• Task 1. Where is this datapoint? The 'leaflet' package.
• Task 2. Show me my data without overcrowding the screen. The 'DT' package.
• Task 3. I'd like to visualise this matrix. The 'd3heatmap' package.
• Task 4. I need interactivity for everything! The 'plotly' package.

Two things you need to construct a basic map:

• Geographical coordinates (and potentially the corresponding data)
• Map style

load('input_output//leaflet_data')
head(map_data, 5)

##       station_name latitude longitude station pressure  time trend_coeff
## 1     PUNTA ARENAS   -53.00    -70.85   85934     2000  noon      -0.276
## 2         MARAMBIO   -64.23    -56.72   89055     2000  noon      -0.060
## 3            SYOWA   -69.00     39.58   89532     2000  noon      -0.666
## 4   AMUNDSEN-SCOTT   -90.00      0.00   89009     2000 night      -0.191
## 5 NOVOLAZARAVSKAJA   -70.77     11.83   89512     2000 night      -0.648

library(magrittr)  #for the %>% pipeline operator
library(leaflet)
leaflet(data = map_data) %>% addTiles() %>%
  addMarkers(~longitude, ~latitude, popup = ~as.character(station_name))

• Can use addProviderTiles(XXX) for other styles of map
• Some options for XXX are "Stamen.Toner", "Acetate.terrain", "CartoDB.Positron"
• Reference: https://rstudio.github.io/leaflet/

leaflet(data = map_data) %>% addProviderTiles("Stamen.Toner") %>%
  addMarkers(~longitude, ~latitude, popup = ~as.character(station_name))

data(iris)
DT::datatable(iris)

library(DT)
datatable(iris, options = list(pageLength = 6)) %>%
   formatStyle('Sepal.Width',
               backgroundColor = styleInterval(3, c('orange', 'white')))

Usage: explore data

data(mtcars)  #built-in dataset in R
library(d3heatmap)
d3heatmap(mtcars, scale = "column", colors = "Spectral")

Usage: explore correlation between predictors

load('input_output//cleaned_data', verbose = T)
library(d3heatmap)
correlation_matrix <- cor(data1[,2:50])
d3heatmap(correlation_matrix, dendrogram = 'none')

## Loading objects:
##   data1

Supports: Matlab, Python, R, Javascript, Excel and Others.

Reference:

1. Documentation @ https://plot.ly/r/reference/
2. Gallery with source codes @ https://plot.ly/feed/

Five components:

1. plot_ly
2. add_trace
3. layout
4. annotation_text
5. other options: how to read the documentation

Next week: ShinyR and Tableau